The Power of Local Information in Social Networks

  • Christian Borgs
  • Michael Brautbar
  • Jennifer Chayes
  • Sanjeev Khanna
  • Brendan Lucier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7695)


We study the power of local information algorithms for optimization problems on social and technological networks. We focus on sequential algorithms where the network topology is initially unknown and is revealed only within a local neighborhood of vertices that have been irrevocably added to the output set. This framework models the behavior of an external agent that does not have direct access to the network data, such as a user interacting with an online social network.

We study a range of problems under this model of algorithms with local information. When the underlying graph is a preferential attachment network, we show that one can find the root (i.e. initial node) in a polylogarithmic number of steps, using a local algorithm that repeatedly queries the visible node of maximum degree. This addresses an open question of Bollobás and Riordan. This result is motivated by its implications: we obtain polylogarithmic approximations to problems such as finding the smallest subgraph that connects a subset of nodes, finding the highest-degree nodes, and finding a subgraph that maximizes vertex coverage per subgraph size.

Motivated by problems faced by recruiters in online networks, we also consider network coverage problems on arbitrary graphs. We demonstrate a sharp threshold on the level of visibility required: at a certain visibility level it is possible to design algorithms that nearly match the best approximation possible even with full access to the graph structure, but with any less information it is impossible to achieve a non-trivial approximation. We conclude that a network provider’s decision of how much structure to make visible to its users can have a significant effect on a user’s ability to interact strategically with the network.


Local Information Local Algorithm Online Social Network Preferential Attachment Good Path 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Christian Borgs
    • 1
  • Michael Brautbar
    • 2
  • Jennifer Chayes
    • 1
  • Sanjeev Khanna
    • 2
  • Brendan Lucier
    • 1
  1. 1.Microsoft Research New EnglandUSA
  2. 2.Computer and Information ScienceUniversity of PennsylvaniaUSA

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